class_docstr: Synthesize audio by filtering white noise.
docstr: Synthesize audio with filtered white noise.
  
  Args:
    magnitudes: Magnitudes tensor of shape [batch, n_frames, n_filter_banks].
      Expects float32 that is strictly positive.
  
  Returns:
    signal: A tensor of harmonic waves of shape [batch, n_samples, 1].
docstr_tok: ["Synthesize","audio","with","filtered","white","noise",".","Args",":","magnitudes",":","Magnitudes","tensor","of","shape","[","batch",",","n_frames",",","n_filter_banks","]",".","Expects","float32","that","is","strictly","positive",".","Returns",":","signal",":","A","tensor","of","harmonic","waves","of","shape","[","batch",",","n_samples",",","1","]","."]
code: def get_signal(self, magnitudes):
      """Synthesize audio with filtered white noise.
  
      Args:
        magnitudes: Magnitudes tensor of shape [batch, n_frames, n_filter_banks].
          Expects float32 that is strictly positive.
  
      Returns:
        signal: A tensor of harmonic waves of shape [batch, n_samples, 1].
      """
      batch_size = int(magnitudes.shape[0])
      signal = tf.random.uniform(
          [batch_size, self.n_samples], minval=-1.0, maxval=1.0
      )
      return core.frequency_filter(
          signal, magnitudes, window_size=self.window_size
      )
  
imports: from ddsp import core
  from ddsp import processors
  import gin
  import tensorflow.compat.v2
label_desc: Use image node_id 3 for calling the FilteredNoise obj's underlying member method code with example usage: obj.get_signal(magnitudes) and returns: core